A Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperative Prediction of DNA Mismatch Repair Deficiency in Gastric Adenocarcinoma

Intratumoral and peritumoral CT-based radiomics for predicting the microsatellite instability in gastric cancer

PURPOSE

To investigate the value of intratumoral and peritumoral radiomics based on contrast-enhanced computer tomography (CECT) to preoperatively predict microsatellite instability (MSI) status in gastric cancer (GC) patients.

METHODS

A total of 189 GC patients, including 63 patients with MSI-high (MSI-H) and 126 patients with MSI-low/stable (MSI-L/S), were randomly divided into the training cohort and validation cohort. Intratumoral and 5-mm peritumoral regions' radiomics features were extracted from CECT images. The features were standardized by Z-score, and the Inter- and intraclass correlation coefficient, univariate logistic regression analysis, and least absolute shrinkage and selection operator (LASSO) were applied to select the optimal radiomics features. Radiomics scores (Rad-score) based on intratumoral regions, peritumoral regions, and intratumoral + 5-mm peritumoral regions were calculated by weighting the linear combination of the selected features with their respective coefficients to construct the intratumoral model, peritumoral model, and intratumoral + peritumoral model. Logistic regression was used to establish a combined model by combining clinical characteristics, CT semantic features, and Rad-score of intratumoral and peritumoral regions.

RESULTS

Eleven radiomics features were selected to establish a radiomics intratumoral + peritumoral model. CT-measured tumor length and tumor location were independent risk factors for MSI status. The established combined model obtained the highest area under the receiver operating characteristic (ROC) curve (AUC) of 0.830 (95% CI, 0.727-0.906) in the validation cohort. The calibration curve and decision curve demonstrated its good model fitness and clinical application value.

CONCLUSION

The combined model based on intratumoral and peritumoral CECT radiomics features and clinical factors can predict the MSI status of GS with moderate accuracy before surgery, which helps formulate personalized treatment strategies.

CT radiomics analysis discriminates pulmonary lesions in patients with pulmonary MALT lymphoma and non-pulmonary MALT lymphoma

PURPOSE

The aim of this study is to create and validate a radiomics model based on CT scans, enabling the distinction between pulmonary mucosa-associated lymphoid tissue (MALT) lymphoma and other pulmonary lesion causes.

METHODS

Patients diagnosed with primary pulmonary MALT lymphoma and lung infections at Fuzhou Pulmonary Hospital were randomly assigned to either a training group or a validation group. Meanwhile, individuals diagnosed with primary pulmonary MALT lymphoma and lung infections at Fujian Provincial Cancer Hospital were chosen as the external test group. We employed ITK-SNAP software for delineating the Region of Interest (ROI) within the images. Subsequently, we extracted radiomics features and convolutional neural networks using PyRadiomics, a component of the Onekey AI software suite. Relevant radiomic features were selected to build an intelligent diagnostic prediction model utilizing CT images, and the model's efficacy was assessed in both the validation group and the external test group.

RESULTS

Leveraging radiomics, ten distinct features were carefully chosen for analysis. Subsequently, this study employed the machine learning techniques of Logistic Regression (LR), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN) to construct models using these ten selected radiomics features within the training groups. Among these, SVM exhibited the highest performance, achieving an accuracy of 0.868, 0.870, and 0.90 on the training, validation, and external testing groups, respectively. For LR, the accuracy was 0.837, 0.863, and 0.90 on the training, validation, and external testing groups, respectively. For KNN, the accuracy was 0.884, 0.859, and 0.790 on the training, validation, and external testing groups, respectively.

CONCLUSION

We established a noninvasive radiomics model utilizing CT imaging to diagnose pulmonary MALT lymphoma associated with pulmonary lesions. This model presents a promising adjunct tool to enhance diagnostic specificity for pulmonary MALT lymphoma, particularly in populations where pulmonary lesion changes may be attributed to other causes.